Background: Hepatocellular carcinoma (HCC) (about 85–90% of primary liver cancer) is particularly prevalent in China because of the high prevalence of chronic hepatitis B infection. HCC is the fourth most common malignancy and the third leading cause of tumor-related deaths in China. It poses a significant threat to the life and health of Chinese people. Summary: This guideline presents official recommendations of the National Health and Family Planning Commission of the People’s Republic of China on the surveillance, diagnosis, staging, and treatment of HCC occurring in China. The guideline was written by more than 50 experts in the field of HCC in China (including liver surgeons, medical oncologists, hepatologists, interventional radiologists, and diagnostic radiologists) on the basis of recent evidence and expert opinions, balance of benefits and harms, cost-benefit strategies, and other clinical considerations. Key Messages: The guideline presents the Chinese staging system, and recommendations regarding patients with HCC in China to ensure optimum patient outcomes.

In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking in MOT, we introduce a cost-sensitive tracking loss based on the state-of-the-art visual tracker, which encourages the model to focus on hard negative distractors during online learning. For data association, we propose Dual Matching Attention Networks (DMAN) with both spatial and temporal attention mechanisms. The spatial attention module generates dual attention maps which enable the network to focus on the matching patterns of the input image pair, while the temporal attention module adaptively allocates different levels of attention to different samples in the tracklet to suppress noisy observations. Experimental results on the MOT benchmark datasets show that the proposed algorithm performs favorably against both online and offline trackers in terms of identity-preserving metrics.

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Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC.

Key Points Question What is the agreement of automatically determined endoscopic severity of ulcerative colitis using deep learning models compared with expert human reviewers? Findings In this diagnostic study including colonoscopy data from 3082 adults, performance of a deep learning model for distinguishing moderate to severe disease from remission compared with multiple expert reviewers was excellent, with an area under the receiver operating curve of 0.97 using still images and full-motion video. Meaning Deep learning offers a practical and scalable method to provide objective and reproducible assessments of endoscopic disease severity for patients with ulcerative colitis.

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The prevalence was: (1) lymphatic spread prone to the upward in the upper oesophageal SCC, downward in the lower one and both up- and downward in the middle one with in favour of the upward and (2) multiple level and skip node metastases were very often seen. The unfavourable factors for node spread were long oesophageal lesion, late T stage and poor differentiation of tumour cells.